Bibliographic record
Abstract
For estimation of linear models with randomly censored data, a class of data transformations is used to construct synthetic data. It is shown that the conditional variance of the synthetic data depends on the covariates in the model regardless of the homoscedasticity of the error. Therefore, linear models based on the synthetic data are always heteroscedastic models. To improve efficiency, we propose a weighted least squares (WLS) method, where the conditional variance of the synthetic data is estimated nonparametrically, then the standard WLS principle is applied to the synthetic data in the estimation procedure. The resultant estimator is asymptotically normal and the limiting variance is estimated using the plug-in method. In general, the proposed method improves the existing synthetic data methods for censored linear models, and gains more efficiency. For the censored heteroscedastic linear models, where the Buckley–James (BJ) and rank-based methods cannot be used since the condition of homoscedastic errors is violated, the new method provides a solution for better estimation. Monte Carlo simulations are conducted to compare the proposed method with the unweighted least squares method and the BJ method under different error conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".